Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, P. R. China.
Xiangya School of Medicine, Central South University, Changsha, P. R. China.
J Cell Mol Med. 2020 Oct;24(19):11607-11612. doi: 10.1111/jcmm.15805. Epub 2020 Aug 31.
Tumour microenvironment of brain lower grade glioma (LGG) consists of non-tumour cells including stromal cells and immune cells mainly. These non-tumour cells dilute the purity of LGG and play pivotal roles in tumour growth and development, thereby affecting patient prognosis. Tumour purity is also associated with molecular subtypes of LGG. In this study, we discovered the most relevant module to purity by weighted gene co-expression network analysis (WGCNA) and afterwards performed consensus network analysis and survival analysis to filter 61 significant genes related to both purity and prognosis. In turn, we built a simplified model based on the calculation of purity score, and consensus measurement of purity estimation (CPE), with a satisfactory predictive performance by random forest regression. HLA-E, MSN, GNG-5, MYL12A, ITGB4, PDPN, AGTRAP, S100A4, PLSCR1, VAMP5 were selected as the most relevant genes correlating to both purity and prognosis. The risk score model based on the 10 genes could moderately predict patients' overall survival. These 10 genes, respectively, were positively correlated positively to immunosuppressive cells like macrophage M2, but negatively correlated to patient prognosis, which may explain partially the poor prognosis with low-purity group.
脑低级别胶质瘤 (LGG) 的肿瘤微环境主要由非肿瘤细胞组成，包括基质细胞和免疫细胞。这些非肿瘤细胞降低了 LGG 的纯度，并在肿瘤的生长和发展中发挥关键作用，从而影响患者的预后。肿瘤纯度也与 LGG 的分子亚型有关。在这项研究中，我们通过加权基因共表达网络分析 (WGCNA) 发现了与纯度最相关的模块，然后进行共识网络分析和生存分析，筛选出与纯度和预后均相关的 61 个显著基因。接着，我们基于纯度得分的计算和纯度估计的共识度量 (CPE) 构建了一个简化模型，通过随机森林回归得到了令人满意的预测性能。HLA-E、MSN、GNG-5、MYL12A、ITGB4、PDPN、AGTRAP、S100A4、PLSCR1 和 VAMP5 被选为与纯度和预后均相关的最相关基因。基于这 10 个基因的风险评分模型可以适度预测患者的总生存率。这 10 个基因分别与巨噬细胞 M2 等免疫抑制细胞呈正相关，但与患者预后呈负相关，这可能部分解释了低纯度组预后不良的原因。
